Functional evaluation proves more comprehensive than purely linguistic metrics in assessing translation quality in the AI-assisted translation era, and is operationalizing Nord's functionalist framework into a measurable evaluation model for systematically comparing HT and MT.
Abstract
The rapid development of neural machine translation has transformed translation practices in academic and professional contexts. However, the ability of machine translation to preserve communicative functions within target texts remains debatable. This study compares the functional quality of Human Translation (HT) and Machine Translation (MT) using Christiane Nord's Functional Typology and functional adequacy framework, emphasizing communicative function, contextual sensitivity, and target-reader orientation as the main indicators of translation quality. A descriptive-comparative design with a mixed-methods approach was employed. Ten English academic texts categorized as informative texts were translated into Indonesian through both human and machine translation. Data were analyzed qualitatively using Nord's extratextual and intratextual factors and quantitatively through a scoring system of five indicators: text-function suitability, semantic accuracy, acceptability, contextual sensitivity, and cohesion-coherence. The findings reveal that Human Translation achieved a mean score of 19.0, categorized as highly adequate, while Machine Translation obtained a mean score of 13.2, categorized as moderately adequate. Human Translation demonstrated superior performance in maintaining academic register and communicative naturalness, whereas Machine Translation tended to produce literal structures with limited contextual adaptation. The novelty of this study lies in operationalizing Nord's functionalist framework into a measurable evaluation model for systematically comparing HT and MT. Functional evaluation proves more comprehensive than purely linguistic metrics in assessing translation quality in the AI-assisted translation era.
The article examines the peculiarities of translating English psychological terminology into Ukrainian and identifies the most effective translation methods used to ensure semantic accuracy and communicative adequacy. The relevance of the study is determined by the growing popularity of psychological literature and the need for a consistent rendering of specialized terminology in Ukrainian. The research aims to analyze the main translation techniques applied to English psychological terms and to determine their frequency and effectiveness. The corpus of the study comprised 128 terms selected from English-language professional psychology texts and their Ukrainian translations. The findings indicate that the most frequently employed techniques are variant equivalent selection (33%), calque translation (25%), and dictionary equivalents (21%). Less common methods include concretization, generalization, contextual substitution, semantic development, descriptive translation, part-of-speech substitution, and transcoding. The study demonstrates that the choice of translation method depends on a term's semantic characteristics, its degree of standardization in the target language, and its use context.
N. Tymoshchuk, U. Shostak· Scientific Journal of Poloni...· 0 citations
This research conducted a comparative analysis of three digital translation tools, Google Translate, Lingvanex, and MateCat, evaluating their performance in translating Saadat Hasan Manto's Urdu short story "Khol Do" into English, indicating that Google Translate is the most successful tool in terms of both quantitative measures and qualitative aspects.
Khadija Mushtaq, Rafia Bilal· Journal of Arts and Linguist...· 0 citations
The results show that LLMs and Google Translate consistently outperform specialized MT systems in terms of fluency, meaning preservation, and lexical-thematic alignment.
Beatriz Ribeiro Borges, P. H. R. Gabriel, E. Faria· International Journal of Dat...· 0 citations
The present study seeks to explore how English as a foreign language (EFL) learner perceive the use of machine translation (MT) for translating words or texts into English. The research type is qualitative descriptive, and the respondents are 13 students who use this technology. Data were collected from machine-supported translation quality assessment and student questionnaires implemented in translation courses. The findings of the research revealed that EFL learners at the university should reconsider and reorganize translation for better results based on their understanding. Machine translation tools are yet another alternative for meaning-making in a foreign language. This MT can also be utilized as a dictionary. Automatic translation is helpful and makes it very easy, as it gives the general meaning of the words. However, students also acknowledge that although machine translation facilitates and expedites their translation tasks, the output is not always accurate in terms of contextual appropriateness and grammatical precision. Overall, despite certain limitations, machine translation remains a highly beneficial tool in language learning and continues to serve as an effective resource for enhancing vocabulary acquisition and supporting comprehension in second language learning contexts.
Keywords: EFL; Machine Translation; Translation Accuracy; Vocabulary learning; student perception
Surtinawati Surtinawati, M. Y. Kardiansyah, Harits Setyawan· ANGLOPHONE: Journal of Engli...· 0 citations
This study examines the extent to which machine-translated and human-translated Croatian–English phrases can be distinguished by expert evaluators by analysing inter-rater agreement. For this purpose, a parallel corpus was compiled from four Croatian research articles in the fields of Education and Psychology, together with their human translations and machine translations produced using the online tool onlinedoctranslator. com. Two professional translators, native speakers of Croatian with extensive translation experience, were asked to classify selected English translations of Croatian phrases as either machine-translated or human-translated. The analysis shows a raw inter-rater agreement of 75 % for both machine-translated and human-translated phrase sets, indicating a moderate level of agreement between the raters. These results suggest that even expert translators do not consistently reach the same conclusions when judging the origin of short, decontextualised translated phrases. The findings support the view that, at the phrase level, machine-translated output is increasingly difficult to distinguish from human translation on the basis of surface linguistic features alone.
Mirjana Borucinsky, Margareta Čanžar· Croatica et Slavica Iadertin...· 0 citations
The integration of machine translation (MT) and artificial intelligence into translation workflows has significantly transformed professional practice, particularly in high-stakes domains such as medical translation. This study investigates professional translators’ perspectives on post-editing (PE) and translation quality assessment (TQA) in English–Romanian medical translation. Drawing on structured interviews and thematic analysis, the findings indicate that MT is primarily used as a support tool due to persistent issues related to terminology and contextual accuracy. Post-editing is considered essential, though often applied without structured adherence to standards such as ISO 18587, while TQA models are unevenly implemented in practice. The study also identifies a significant gap between academic training and professional requirements, particularly regarding technological and domain-specific competences. These findings highlight the need for a more integrated and practice-oriented approach to translator training in the Romanian context.
Z. Kovács, Daniel Dejica· Scientific Bulletin of the P...· 0 citations